# CuPy-Xarray: Xarray on GPUs!

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## Overview

CuPy-Xarray is a Python library that leverages [CuPy](https://cupy.dev/), a GPU array library, and [Xarray](https://docs.xarray.dev/en/stable/), a library for multi-dimensional labeled array computations, to enable fast and efficient data processing on GPUs. By combining the capabilities of CuPy and Xarray, CuPy-Xarray provides a convenient interface for performing accelerated computations and analysis on large multidimensional datasets.

## Installation

> `cupy-xarray` will use an existing cupy installation, hence cupy needs to be installed manually! Please follow cupy's install instructions at <https://docs.cupy.dev/en/stable/install.html>.

CuPy-Xarray can be installed using `pip` or `conda`:

From Conda Forge:

```bash

conda install cupy-xarray -c conda-forge
```

From PyPI:

```bash
pip install cupy-xarray
```

The latest version from Github:

```bash
pip install git+https://github.com/xarray-contrib/cupy-xarray.git
```

## Acknowledgements

Large parts of this documentations comes from [SciPy 2023 Xarray on GPUs tutorial](https://negin513.github.io/cupy-xarray-tutorials/README.html) and [this NCAR tutorial to GPUs](https://github.com/NCAR/GPU_workshop/tree/workshop/13_CuPyAndLegate).

## Contents

```{eval-rst}

**User Guide**:

.. toctree::
   :maxdepth: 1
   :caption: User Guide

   examples/01_cupy-basics
   examples/02_introduction
   examples/03_basic-computations
   examples/04_high-level-api
   examples/05_apply-ufunc
   examples/06_real-example
   examples/07_kvikio


**Tutorials & Presentations**:

.. toctree::
   :maxdepth: 1
   :caption: Tutorials & Presentations

   source/tutorials-and-presentations

**Contributing**:

.. toctree::
   :maxdepth: 1
   :caption: Contributing

   source/contributing


**Reference**:

.. toctree::
   :maxdepth: 1
   :caption: Reference

   api
   changelog
```